International Journal of Artificial Intelligence, Data Science, and Machine Learning
Generative AI for Financial Excellence: Enhancing Accounting, FP&A, and Compliance across Business Functions
It is self-generated that the new AI, also known as generative AI, is disrupting industries through new productivity and possibilities. In the field of finance, it is very influential in enhancing efficiency in the undertaking of activities, and it also plays a big role in making decisions and ensuring compliance is adhered to. This paper explores the application of generative AI in three critical financial domains, including accounting, FP&A, and compliance. Therefore, there are specific benefits businesses and organizations can derive from integrating AI into their accounting strategy, and they include: The approach used in the methodology entails the use of AI algorithms in live applications with the intention of evaluating their performance. From the outcomes presented, the degrees of efficiency increase, accuracy enhancement and compliance with the relevant regulation are apparent. Finally, this paper presents a research agenda or direction for the existing advancements of these technologies, as well as their ramifications on the business world and its environment.
AI-Driven Energy Optimisation for Enterprise Integration Platforms: A Framework for Sustainable and Intelligent Digital Enterprises
Enterprise integration platforms increasingly rely on artificial intelligence to coordinate energy-intensive digital infrastructure across cloud, edge, and on-premises environments. This article presents a layered framework in which predictive analytics, reinforcement learning, and digital twin technologies operate within enterprise resource planning, middleware, and Internet of Things layers to reduce electricity consumption while preserving operational performance. Evidence gathered across recent scholarship shows that intelligent energy platforms lower total energy use, improve forecasting accuracy, and strengthen resilience against demand fluctuations without materially degrading service quality. Edge intelligence brings computation closer to data sources, cutting latency and easing the load placed on centralized infrastructure, while thematic analyses of the wider literature confirm that energy optimization, ethical governance, and computational efficiency now define the core research agenda for sustainable artificial intelligence. Enterprise-scale validation demonstrates that carefully tuned optimization algorithms achieve substantial reductions in energy consumption with only marginal effects on accuracy or throughput, and coordinated software engineering practices extend these gains through standardized benchmarking and sustainability-aware architecture. Circular economy applications further show that multi-layered optimization of resources, materials, and computing assets improves recovery efficiency alongside energy savings, and digital twin integration across smart and green buildings extends these benefits to the built environment that houses enterprise systems. Together, these findings indicate that intelligent, integrated energy platforms deliver measurable environmental and financial value for digital enterprises. Organizations that adopt layered artificial-intelligence architectures gain a practical pathway toward lower operating costs, stronger regulatory compliance, and more resilient digital operations, positioning energy-aware design as a core element of enterprise technology strategy rather than a peripheral concern.
A Deep Learning-Based Intelligent Road Safety Framework Using Multi-Source Traffic, Weather, and Accident Data
Road traffic accidents remain a major public safety challenge worldwide, resulting in substantial human and economic losses. Existing road safety systems primarily rely on historical accident records and often fail to provide proactive risk prediction under dynamic traffic and environmental conditions. This paper proposes a deep learning-based intelligent road safety framework that integrates heterogeneous data sources, including traffic flow, weather conditions, historical accident records, road geometry, temporal traffic patterns, and geographic information, to predict accident risks in real time. The proposed framework employs a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to capture both spatial and temporal dependencies within multi-source transportation data. To improve model interpretability and support reliable decision-making, Explainable Artificial Intelligence (XAI) using SHapley Additive exPlanations (SHAP) is incorporated to identify the most influential factors contributing to accident occurrence. The framework generates dynamic road risk scores and identifies accident-prone road segments, enabling proactive traffic management, optimized emergency response, and data-driven infrastructure planning. Experimental evaluation using publicly available traffic, weather, and accident datasets demonstrates that the proposed model outperforms conventional machine learning approaches, including Random Forest, Support Vector Machine, and XGBoost, in terms of prediction accuracy, robustness, and interpretability. The proposed framework provides a scalable and practical solution for next-generation Intelligent Transportation Systems (ITS), contributing to safer, more efficient, and resilient smart transportation networks.
Intelligent Transportation Systems (ITS)
Explainable Artificial Intelligence (XAI)
Cyber Defense Digital Twins: A Quantitative, AI-Driven Risk Intelligence and Compliance Automation Framework Spanning Enterprise Controls and Third-Party Vendor Risk
Modern enterprise networks operate under persistent threats that exploit cloud-native misconfigurations, identity sprawl, and API vulnerabilities at machine speed. Existing security operations center (SOC) architectures remain largely reactive, signature-dependent, and incapable of predicting multi-stage lateral movement. This paper proposes the Cognitive Cyber Defense Digital Twin (CCDT), a unified architecture integrating federated learning (FL), graph neural network (GNN)-based attack-path forecasting, adversarial hardened detection models, and autonomous Security Orchestration, Automation, and Response (SOAR) with deception engineering. The CCDT constructs a continuously synchronized digital replica of organizational assets and employs reinforcement learning-based red agents to stress-test detection models. A federated intelligence mesh enables cross-organizational privacy-preserving gradient sharing. Experimental evaluations against CICIDS-2018 and LANL datasets demonstrate 52% faster attack-path detection, 41% reduction in false positive rate, and 60% reduction in mean-time-to-respond (MTTR) compared to traditional SOC baselines. Integrated Explainable AI (XAI) modules using SHAP values enable audit-ready compliance reporting. The CCDT represents a paradigm shift from reactive monitoring to predictive, autonomous, and privacy-preserving cyber defense for hybrid cloud environments.
ML-Powered Data Catalogs and Metadata Inference for Self-Describing Big Data Ecosystems
In today's fast-changing data world, businesses have too much data that is too different and spread out. Standard methods for managing data don't always work well for finding, understanding, and applying data. Modern data catalogues are more than simply static lists of items; they are also dynamic places to store information that provide structure, meaning, and easy access to large data ecosystems. These catalogues can really shine when ML is applied to figure things out smartly, even when the data sets are too messy, not well-documented, or not well-organized. Data catalogues that utilize ML look at these current data trends, user behaviors & contextual signals to automatically tag, categorize & link their data assets. This makes the data self-descriptive and makes governance easier. This solves a big problem: creating information by hand is hard work, prone to their mistakes, and typically doesn't work well with different teams & also technologies. Our methodology utilizes both supervised and unsupervised ML techniques to infer schema details, data lineage, semantic links, and use contexts, therefore reducing dependence on human input while improving their data discoverability & the dependability. This paper suggests a scalable architecture that integrates ML models directly into the cataloguing process, enabling actual time metadata enhancement as data is ingested into the system. Additionally, we have a feedback mechanism that constantly improves the quality of inferences via user interactions & validations. Initial findings indicate significant improvements in data discoverability, accelerated onboarding for data users, and improved compliance readiness, resulting from enhanced metadata completeness and traceability. This work shows how data catalogues that use machine learning can change from simple documentation tools into these smart systems that make self-service analytics easier, promote data democracy & support truly self-describing big data platforms. This makes organizations more agile and focused on getting insights.
AI-Driven Auto ML for Analytics Workflow Acceleration on Distributed Data Lakes
As organizations generate data from numerous sources, which have progressively become larger in scale ranging from IoT devices to cloud applications the typical centralized data warehouse has been replaced by the distributed data lakes. Such repositories are cloud-based and therefore offer flexible and scalable storage for structured and unstructured data. However, distributed data lakes, besides being a perfect solution for storage and access issues, still bring some adverse effects: the capability to execute the analytical workflows has become more complex, resource-consuming, and hard to control when scaling. Launching and implementing machine learning models in these kinds of places requires, most of the time, a great deal of time, high-level professional skills, and good coordination between different teams. AutoML (Automated Machine Learning) is a newly appearing possibility that aims to solve major problems. It does so by carrying out the laborious stages of feature selection, model training, and hyperparameter tuning—that greatly facilitates the implementation of analytics and makes it less time-consuming but it is not a perfect solution. In order to get the utmost benefit from AutoML in distributed environments, AI-driven orchestration is necessary. With the help of intelligent agents, the pipeline gains real-time decision power about power distribution, routing, and failure recovery; thus, static processes become more dynamic and self-optimized. This paper is a discussion of a combination of AI-driven AutoML frameworks with dispersed data lake architectures to make analytics pipelines easier and faster.
Predictive Drift Detection and Adaptive Reconciliation in Multi-Cloud Data Environments
In the present day's multi-cloud environments, it has become very hard to keep data consistent & reliable because of the unexpected phenomenon of data drift, which is when the basic structure, quality, or meaning of data changes suddenly across these distant settings. This study offers a way to forecast these kinds of drifts before they have an effect on the important systems. Instead of reacting to many problems after they happen, our solution uses ML models based on the previous information, schema history & change patterns to detect too many problems before they happen. Our adaptive reconciliation engine automatically syncs data sources using resolution methods that take into account the context. It also changes to fit the formats and workloads of the cloud. Companies that use hybrid and multi-cloud architectures need to be able to make decisions, audit, and govern in real time more and more. This dual-layered solution is based on that requirement. Our system brings together cloud-native monitoring tools, statistical drift indicators, and proactive repair methods into a single lifecycle. Early research shows that our system can predict schema and distributional deviations with more than 90% accuracy. The reconciliation layer also cuts down on the need for human input by more than 60%. Some of the most important contributions include the latest predictive model for drift predictions, a reconciliation protocol that can be used on a wide range of cloud platforms, and a single dashboard for operational transparency. The goal of this project is to improve the data governance in these organizations, build trust in the analytical outputs & lower their compliance worries. Our technique is a big step forward in how organizations keep data safe in dynamic multi-cloud systems. Instead of waiting for these kinds of problems to happen and then fixing them, we now anticipate and solve them before they happen.
Real-Time Analytics at Enterprise Scale: Leveraging Kafka, Spark, and Cloud Data Lakes for Business Intelligence
Real-time analytics is a critical capability for today’s enterprises, which want to accelerate data-driven decision-making in an increasingly dynamic business world. Organizations produce a large amount of data from customer interactions, IoT devices, digital platforms and operational systems. Traditional batch-processing approaches are no longer sufficient to deliver timely and scalable insights that are critical for competitive advantage. This paper studies the integration of Apache Kafka, Apache Spark and Cloud Data Lakes as a unified architecture for enterprise-level real-time analytics and business intelligence. This work is motivated by the increasing demand for scalable, fault resistant and high-performance data processing frameworks that can handle continuous streams of structured and unstructured data. Apache Kafka is a distributed event streaming technology for reliable data ingestion and real-time message processing and Apache Spark enables large-scale stream analytics, machine learning and fast data transformation. A Cloud Data Lake offers scalable, low-cost storage and centralized control of data with the ability to analyze it in real-time or over time. The primary objective of the research is to evaluate the impact of the use of several technologies together on improving the data availability, ease of processing, and decision-making capability of organizations. An approach based on architecture analysis, system integration, performance evaluation and real use cases of business intelligence is proposed to evaluate scalability, latency, throughput and operational efficacy. The results show that the combination of Kafka, Spark and Cloud Data Lakes significantly improves the speed of data processing, allows near real-time insights, reduces infrastructure complexity and provides seamless scalability inside cloud-native systems. Moreover, the proposed framework enhances organizational agility by allowing organizations to respond proactively to market developments, customer behavior and operational events. The research provides a practical, scalable reference architecture for firms seeking to modernize their analytics platforms and highlights best practices for building real-time data pipelines that enable meaningful business insight and long-term digital transformation.
AI-Augmented CRM Transformation: Leveraging Microsoft Dynamics 365, Power Platform, and Cloud Intelligence for Next-Generation Customer Engagement
Artificial Intelligence (AI) is a powerful force in Customer Relationship Management (CRM), also known as the re-invention of CRM to make it more intelligent, predictive and personalized. Here in this paper, we discuss the use of multiple technologies such as Microsoft Dynamics 365, Microsoft Power Platform, Azure AI and cloud intelligence to the complete CRM transformation of the enterprises of the future. AI-driven CRM systems are becoming indispensable tools for companies as they help to enhance customer experience, increase operational efficiency, support decision-making, and boost business agility especially in the face of ever more competitive digital market situations. Employing a qualitative case study methodology, the research was conducted on an AI-empowered CRM system designed with Dynamics 365 and Power Platform, equipped with Azure AI, machine learning, automation, and cloud-based analytics capabilities. To gain a deep understanding of the effect of intelligent automation and predictive insights on customer engagement processes data was collected through system performance assessments, user feedback, workflow evaluations, and business outcome measurements. The case illustrates that AI-driven features such as lead scoring, sentiment analysis of customers, conversational agents, forecasting, and automated service workflows significantly drive the value of customer engagement through an enhanced and personalized experience. The major results indicate substantial progress in customer experience, response times, sales efficiency, service effectiveness, and decision-making based on data. Besides, the changing facet of the low-code development through Power Platform in breaking the grounds and empowering business users also featured prominently in the research. More importantly, cloud intelligence facilitates scalable, secure, and real-time access to customer information that leads to a continuous cycle of business improvement. This paper sheds light on the growing area of AI-enabled digital transformation by outlining a viable framework for combining CRM, AI, automation, and cloud technologies within a single ecosystem.
Transformation to Quality Engineering through AI with Automation using Machine Learning
The application of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized quality engineering by making intelligent automation, predictive analytics and adaptive software testing possible. However, traditional quality engineering techniques can often prove difficult to implement, scale, integrate and control defects in modern software environments. In this study, we present a novel quality engineering transformation framework through the integration of machine learning and automated quality assurance processes to increase software reliability, reduce test time and improve prediction capabilities for software defects. The AI-enabled framework presented in the research uses intelligent analytics to perform adaptive risk assessment, automated optimization of test cases and quality monitoring at all stages of software engineering. Furthermore, the framework makes use of the behavioral learning model to optimize testing techniques based on previous project experiences and knowledge. The benefits of the experiment in comparison with traditional quality engineering techniques can be easily illustrated with the help of experimental findings; they include increased efficiency and accuracy in detecting software defects, resource optimization, faster test processing and improved overall software quality.
The Economic Impact of AI Adoption in Healthcare: Estimating National Cost Savings, Productivity Gains, and Long-Term Health Outcomes
Artificial intelligence (AI) is increasingly being adopted across healthcare systems to improve diagnostic accuracy, streamline administrative processes, strengthen clinical decision-making, and support preventive care. However, its national economic implications remain insufficiently understood, particularly regarding healthcare cost savings, workforce productivity, and long-term population-health outcomes. This study develops an evidence-informed national economic modelling framework to estimate the potential impact of AI adoption across key healthcare functions, including diagnostic imaging, automated screening, clinical decision support, predictive risk management, telehealth, and administrative automation. The framework compares current healthcare delivery with low-, moderate-, and high-adoption AI scenarios over a ten-year period. Economic outcomes include direct medical cost savings, reduced avoidable hospitalizations, improved diagnostic efficiency, clinician and administrative time savings, increased service capacity, and long-term health benefits measured through avoided complications and quality-adjusted life years. The analysis also incorporates implementation, maintenance, workforce-training, data-infrastructure, and governance costs to estimate net economic value. The study argues that AI can generate substantial national value when deployed in high-volume, high-cost, and prevention-oriented services. Nevertheless, financial benefits depend on interoperable health-data systems, clinical integration, algorithmic accuracy, human oversight, equity safeguards, and continuous performance monitoring. The proposed framework offers policymakers a practical basis for prioritizing responsible AI investments that improve both healthcare efficiency and long-term patient outcomes.
Generative AI and Agentic Orchestration for Autonomous Data Engineering in Multi-Domain Enterprise Analytics Platforms
Enterprise data engineering platforms operating across banking, insurance, finance, and manufacturing domains face escalating complexity in managing heterogeneous data pipelines, dimensional data models, and governance workflows that demand expert human oversight at every stage of the software development life cycle. The emergence of generative artificial intelligence (GenAI) and agentic AI systems introduces a transformative opportunity to autonomize requirements elicitation, ETL pipeline synthesis, dimensional schema generation, data quality enforcement, and reconciliation orchestration - capabilities that have historically required deep domain expertise accumulated over decades of practice. This paper presents a novel Agentic Data Engineering Orchestration Framework (ADEOF) that positions large language model (LLM)-powered agents as autonomous actors across the SDLC, coordinating requirements gathering from functional leaders and subject matter experts, auto-generating ETL technical design documents, synthesizing star and snowflake schema designs from natural language business requirements, and autonomously executing data quality validation and gap analysis workflows. The framework introduces a multi-agent coordination protocol wherein specialized sub-agents - a Requirements Elicitation Agent, a Schema Synthesis Agent, an ETL Orchestration Agent, and a Reconciliation Governance Agent - operate under a central orchestrator implementing a Directed Acyclic Graph (DAG)-based task decomposition model. Empirical evaluation across representative enterprise data engineering scenarios demonstrates a 64% reduction in requirements-to-design cycle time, a 71% improvement in ETL mapping completeness, and an 83% reduction in dimensional modeling defect rates compared to fully manual baselines. The proposed framework advances the state of autonomous enterprise data engineering and establishes GenAI-agentic architectures as a credible foundation for next-generation intelligent data platforms. Keywords: generative AI, agentic AI, ETL automation, dimensional modeling, autonomous data engineering, multi-agent orchestration, enterprise data platforms, LLM-driven SDLC, data governance automation.
AI-Augmented Fund Accounting Repository for Governance-Driven Billing Automation
Enterprise financial institutions managing global fund accounting operations face compounding challenges arising from the fragmentation of account, asset, transaction, security, position, and valuation data across heterogeneous source systems, leading to billing inaccuracies, audit exposure, and inadequate traceability of fee computations through Oracle Revenue Management and Billing (ORMB) platforms. Existing siloed ETL pipelines and monolithic data warehouse architectures fail to provide the lineage transparency, reconciliation depth, and control-plane intelligence necessary to satisfy regulatory audit requirements and operational risk mandates at scale. This paper proposes an AI-augmented centralized fund accounting data repository architecture that integrates governance-aware ETL orchestration, automated data quality enforcement, exception-driven reconciliation, and traceability-preserving billing pipelines to eliminate systemic control gaps in global fee billing workflows. The proposed system introduces a five-layer control architecture spanning sourcing, transformation, reconciliation, billing alignment, and audit readiness, augmented by machine learning anomaly detection at the reconciliation layer to proactively identify valuation and position breaks prior to invoice generation. Empirical evaluation demonstrates a 68% reduction in billing exception rates, a 74% improvement in reconciliation cycle times, a 91% increase in audit evidence completeness, and a 55% reduction in operational risk incidents associated with data breaks. The architecture is validated against industry-standard fund accounting governance frameworks and demonstrates broad applicability to custodian banks, asset managers, and global fund administrators operating at enterprise scale. Keywords: fund accounting data repository, ORMB billing governance, ETL orchestration, data lineage, reconciliation automation, AI-driven anomaly detection, enterprise financial data platforms.
A Reconciliation-Driven Methodology for Legacy-to-Cloud Data Warehouse Migration: A Framework for the Enterprise Reporting Platform
Government health-benefit systems accumulate decades of fragmented data across relational databases, flat files, and unstructured documents, creating risk when modernizing to cloud-native analytics platforms. This article presents a reconciliation-driven migration methodology applied to the organization Enterprise Reporting Platform, migrating legacy Microsoft SQL Server data, CSV/Excel files, and PDF-sourced records into a Snowflake warehouse on AWS infrastructure. Existing approaches prioritize extraction throughput over validation, leaving completeness and rule-conformance checks as a manual, post-hoc activity-a gap that is costly in regulated, multi-source government environments spanning domains such as CKMMD, MMD, TMED, TPEAS, DSNS, and Communications/IC Request processing. The proposed methodology embeds profiling, staged extraction, auditable loading, and rule-based reconciliation as concurrent, automated pipeline stages rather than sequential afterthoughts, orchestrated through AWS Glue, staged in S3 and RDS, and validated within Snowflake prior to Power BI consumption. Results across six domains show completeness improving from a baseline of 91.4% to 99.6%, a 73% reduction in manual validation effort, and load cycle time reduced by 61% versus a sequential baseline. These results indicate embedding reconciliation directly into the pipeline-rather than as a terminal audit step-produces measurably higher data trustworthiness at lower operational cost, offering a transferable pattern for other state-level health and human-services modernization programs.
Benchmarking Hybrid On-Device and Cloud AI Inference for Android and Embedded Devices: Latency, Memory, Energy, Privacy, and Accuracy Trade-offs
Android phones, automotive displays, wearables, home devices, retail terminals, industrial gateways, and other embedded gadgets increasingly run artificial intelligence workloads that were previously hosted almost exclusively in the cloud. On-device inference improves privacy, offline behavior, and round-trip latency, but it is constrained by model size, memory, battery, thermal limits, accelerator availability, and fragmented hardware. Cloud inference supports larger models and centralized updates, but it introduces network latency, availability risk, data-transfer cost, privacy exposure, and service dependency. This paper proposes a benchmarking and decision framework for hybrid on-device and cloud AI inference on Android and embedded devices. The framework evaluates five deployment modes: fully on-device inference, cloud-only inference, local prefiltering with cloud escalation, confidence-based hybrid routing, and cloud fallback after local failure. It defines workload classes, device classes, network profiles, privacy levels, and metrics including p50/p95 latency, time-to-first-token, throughput, peak RAM, model size, energy per inference, quantization accuracy loss, network round-trip delay, and privacy exposure. The paper also presents a routing algorithm that selects the inference location based on latency budget, privacy class, model memory footprint, battery state, network quality, and confidence threshold. The proposed framework is aligned with the technology landscape available by April 2025, including LiteRT/TensorFlow Lite foundations, Android on-device AI APIs, Gemini Nano experimental access, Gemma 3 class open models, ONNX Runtime Mobile, and MLPerf-style inference benchmarking.
An Enterprise-Scale Retrieval-Augmented Generative Intelligence Platform for Real-Time Financial Document Synthesis and Regulatory Compliance Automation
Financial services enterprises process vast volumes of unstructured documents-loan agreements, compliance filings, audit reports, and customer correspondence-requiring synthesis, summarization, and regulatory cross-referencing under strict accuracy and traceability constraints. Conventional document automation relies on rule-based extraction or single-pass large language model (LLM) summarization, both of which struggle with provenance tracking, multi-document reasoning, and auditability demanded by financial regulators. This paper presents the Enterprise Generative Document Intelligence Platform (EGDIP), a layered system architecture integrating retrieval-augmented generation (RAG), microservices-based orchestration, and containerized deployment to deliver scalable, traceable document synthesis for regulated financial environments. EGDIP organizes processing into four cooperating layers: a data ingestion layer supporting heterogeneous document formats and streaming updates, a retrieval and indexing layer built on vector-based semantic search, an intelligence layer combining a fine-tuned LLM with deterministic compliance-rule validation, and a consumption layer exposing synthesized outputs through RESTful APIs and interactive dashboards. The system was deployed on a containerized Kubernetes environment and evaluated on a corpus of 180,000 financial documents from loan servicing and regulatory filing workflows. Results show a 58.3% reduction in document review time, a 39.7% improvement in cross-reference accuracy relative to keyword-based retrieval baselines, and sub-second retrieval latency at the evaluated scale. These findings demonstrate that combining retrieval-augmented generation with deterministic validation yields a practical, auditable path toward generative AI adoption in compliance-sensitive enterprise settings.
Enterprise-Wide Outstanding Management Platform: AI and Cloud-Native Platform for Real-Time Governance Visibility in Financial Infrastructure
Large financial institutions operate under continuous compliance, security, and vendor risk obligations distributed across hundreds of teams, systems, and jurisdictions. The absence of a unified operational exception tracking layer forces organizations to manage compliance training delinquencies, project-level vulnerability backlogs, end-of-vendor-support risks, and operational exceptions across disconnected spreadsheets, siloed portals, and manual email chains-producing critical blind spots in governance visibility for senior leadership. This paper presents the Distributed Outstanding Management Platform (DOMP), an enterprise-wide cloud-native system designed and deployed at Credit Suisse to aggregate, normalize, track, and govern operational exceptions sourced from multiple upstream risk and compliance systems. DOMP integrates a Multi-Source Data Normalization Engine (MSDNE), Configurable Delegation and Escalation Protocol (CDEP), Real-Time Governance Visibility Layer (RGVL), and Automated Notification Orchestration Engine (ANOE) within a microservices architecture deployed on OpenShift/Kubernetes. Data exchange is orchestrated via IBM MQ, REST APIs, and SFTP, with AWS S3 as the central staging substrate and Oracle as the authoritative persistence layer. A React-based web portal provides role-scoped dashboards for managers and delegates, while automated email workflows drive accountability without portal dependency. Operational evaluation demonstrates processing of high-volume daily outstanding records across four exception categories, sub-3-second real-time dashboard refresh, delegate resolution tracking at 98.4% audit completeness, and a 67% reduction in governance blind spots compared to the prior fragmented approach. DOMP establishes a replicable architectural blueprint for enterprise operational risk aggregation in regulated financial environments.
Transformer-Based Multi-Signal Predictive Autoscaling for SLA-Aware Resource Management in Kubernetes-Orchestrated Cloud-Native Environments
Kubernetes has become the dominant orchestration substrate for cloud-native applications, yet its native autoscaling mechanisms remain primarily reactive, threshold-driven, and limited in their ability to anticipate workload volatility before service-level agreement violations occur. Modern microservice systems exhibit non-linear interactions among request arrival rates, queueing delays, CPU saturation, memory pressure, network variability, pod cold-start latency, and downstream dependency bottlenecks. These characteristics make single-metric autoscaling policies insufficient for latency-sensitive workloads operating under strict service-level objectives. This paper proposes a Transformer-Based Multi-Signal Predictive Autoscaling framework for SLA-aware resource management in Kubernetes-orchestrated cloud-native environments. The proposed framework integrates heterogeneous observability signals, multi-horizon time-series forecasting, uncertainty-aware decision logic, and Kubernetes-native actuation to allocate resources before overload conditions materialize. Unlike conventional Horizontal Pod Autoscaler configurations that respond after resource utilization crosses predefined thresholds, the proposed approach forecasts near-future demand and performance risk using a Transformer encoder architecture designed to learn long-range dependencies, temporal seasonality, burst behavior, and cross-metric interactions. The framework translates predicted workload and latency risk into safe scaling actions through policy constraints that consider replica bounds, cooldown windows, pod readiness delays, cost budgets, and SLA violation probability. The paper develops the conceptual architecture, methodological workflow, evaluation metrics, and analytical discussion necessary for empirical implementation. The study argues that SLA-aware predictive autoscaling should be treated not merely as a forecasting task but as an integrated control problem involving observability quality, model calibration, decision governance, and runtime safety. The proposed model contributes to cloud resource management research by aligning deep temporal learning with Kubernetes operational semantics and by providing a structured pathway toward more reliable, efficient, and self-adaptive cloud-native platforms.
Converging Infrastructure and Security: A Maturity-Based Approach to Cloud-Native Data Protection, SIEM Optimization, and Compliance Automation
Modern enterprises increasingly struggle to manage cloud security architecture, infrastructure resilience, SIEM operations, and regulatory compliance as isolated disciplines, resulting in operational inefficiencies, increased cyber risk, and costly audit processes. This paper presents a comprehensive five-level maturity model that unifies these traditionally disconnected domains into a cohesive framework for enterprise cybersecurity transformation. The proposed maturity model comprising Fragmented, Instrumented, Correlated, Automated, and Adaptive stages provides organizations with a practical roadmap for assessing current capabilities and systematically advancing toward intelligent, self-optimizing security operations. Unlike conventional reference architectures that assume green field deployments, the framework addresses the realities of heterogeneous enterprise environments spanning multi-cloud platforms, storage infrastructures, backup systems, security information and event management (SIEM) solutions, and compliance programs. The study further introduces diagnostic decision flows, capability maps, maturity transition guidance, and comparative operational metrics demonstrating improvements in incident detection, containment, backup resilience, compliance coverage, and automation maturity. By emphasizing the convergence of cloud infrastructure, cybersecurity operations, data protection, and governance through automation and cross-functional collaboration, the proposed framework enables organizations to reduce operational complexity, strengthen cyber resilience, accelerate regulatory compliance, and establish adaptive security capabilities suitable for modern cloud-native enterprises.
Security Information and Event Management (SIEM)
A Hybrid Data Engineering and Generative AI Architecture for Intelligent Data Governance, Metadata Management, and Automated Data Quality Assessment on AWS
The increasing complexity of enterprise data ecosystems has thrown new challenges at the problem of data governance, metadata management and data quality assurance. Cloud-based platforms are becoming more and more important for organizations to store, process and analyze massive amounts of structured, semi-structured and unstructured data from business applications, Internet of Things (IoT) devices, customer interactions, social media, and transactional systems. Cloud technologies offer scalable infrastructure for data management, but traditional governance practices can find it challenging to ensure high-quality data, enforce compliance policies and maintain consistency of metadata in distributed environments. The adoption of data-driven decision-making has created a critical need for more intelligent, automated and scalable governance mechanisms as enterprises go through this transition.With the transition to data-driven decision-making processes, the need for more intelligent, automated and scalable governance mechanisms has become critical. With the recent development of Generative Artificial Intelligence (GenAI), the ways in which traditional data engineering practices can be improved by augmenting them with automated metadata generation, data cataloging, data quality checks, anomaly detection, and enforcement of governance policies have expanded. By combining Generative AI with cloud-native data engineering services, organizations can develop self-managing data ecosystems that can sense the context of data, develop semantic metadata, self-identify data quality problems, and autonomously recommend solutions to the problem with minimal human input. AWS Glue, Amazon S3, Amazon Lake Formation, Amazon Athena, Amazon Redshift, Amazon Lambda, Amazon Bedrock, and Amazon SageMaker are all components of a complete suite of cloud services available from Amazon Web Services (AWS) that can be deployed as part of an intelligent governance framework. We propose a Hybrid Data Engineering and Generative AI Architecture for Intelligent Data Governance, Metadata Management and Automated Data Quality Assessment on AWS. The suggested framework involves automating data ingestion pipelines, extracting metadata, orchestrating governance processes, implementing Generative AI-based semantic understanding systems, and incorporating machine learning-based data quality evaluation tools. The architecture can be automated to classify data sets, create business metadata, validate policies, discover data lineage, score quality, identify anomalies, and report on governance. These generative AI models are being used for schema interpretation, business description, identification of sensitive information, and governance actions recommendation based on organizational policies. This architecture has four main components: Data Engineering Layer, Metadata Intelligence Layer, Generative AI Governance Layer, and Automated Data Quality Assessment Layer. Together these layers help to achieve data lifecycle management and enhance governance, compliance, metadata completeness, and data reliability. Experimental results indicate that metadata quality accuracy, metadata governance automation, precision of metadata quality assessment, precision of anomaly detection performance and speed of operations are greatly enhanced over traditional governance systems. The proposed architecture helps create an intelligent, scalable and cloud-native governance ecosystem to support the modern enterprise data management. By combining data engineering methods and Generative AI capabilities, businesses can shift the data governance model from a reactive administrative process to a proactive and intelligent decision support system. These results show that AI governance models can significantly improve data asset trustworthiness, availability, and business value, while minimizing governance complexity and costs.
AI-Augmented Software Quality Engineering: Automated Testing, Defect Prediction, and Reliability Optimization for Cloud-Native Applications
The rapid adoption of cloud-native architectures has fundamentally transformed software engineering practices by introducing highly distributed, scalable, and dynamically evolving application environments. While cloud-native applications offer flexibility, elasticity, and resilience, they also introduce significant challenges related to software quality assurance, defect management, testing complexity, and system reliability. Traditional software quality engineering approaches often struggle to cope with the velocity and complexity of modern DevOps and continuous deployment ecosystems. In this context, Artificial Intelligence (AI) has emerged as a transformative technology capable of augmenting software quality engineering processes through automated testing, intelligent defect prediction, and reliability optimization mechanisms. This research investigates the integration of AI-driven methodologies within software quality engineering frameworks for cloud-native applications. The study examines machine learning-based automated testing strategies, predictive analytics models for software defect identification, and AI-enabled reliability enhancement techniques designed for distributed cloud environments. A conceptual framework is proposed that integrates automated testing, defect prediction, and reliability optimization into a unified quality engineering lifecycle. The research methodology employs a comprehensive literature analysis and comparative evaluation of contemporary AI-assisted software quality techniques. The findings indicate that AI-driven testing significantly improves test coverage and execution efficiency, while predictive defect analytics reduces maintenance costs and accelerates fault detection. Furthermore, reliability optimization algorithms enhance system availability and service resilience by proactively identifying performance anomalies and infrastructure failures. The study contributes a holistic perspective on AI-augmented software quality engineering and highlights emerging opportunities for intelligent quality assurance in cloud-native software ecosystems.
The Post-Human Interface: Rethinking Data Architecture for Autonomous Agentic Workflows
Modern data architecture is fundamentally constrained by a legacy assumption: that the ultimate con- sumer of information is a human being. Consequently, cur- rent paradigms disproportionately allocate computational resources to human-legible interfaces, rigid API schemas, and highly abstracted intermediate data states designed primarily for manual oversight and debugging. With the rapid ascendancy of autonomous agentic workflows, this human-centric design introduces severe inefficiencies in latency, throughput, and structural complexity. (1) This paper introduces an Agent-First Data Architecture (AFDA), a paradigm shift that re-engineers data stor- age, transport, and synthesis exclusively for machine-to- machine optimization. We explore the systematic disman- tling of the traditional visual application layer, demon- strating how autonomous agents render fixed user in- terfaces and rigid middleware pipelines obsolete through on-the-fly, task-specific data computation. Furthermore, we analyze the efficiencies gained by transitioning from human-readable protocols (such as JSON or XML) to non- human-readable intermediate states. Finally, we address the architectural flattening of the modern software stack and confront the emerging challenges of this shift, specif- ically the ”black box” debugging crisis and the necessity of specialized Observer Agents for forensic translation. Ultimately, we argue that shifting from a human-centric to an agent-first paradigm is a prerequisite for unlocking the true scaling laws of decentralized machine intelligence. (2)
Unsupervised Learning for Customer Behavior Analysis: A Clustering Approach
Understanding customer purchasing behavior is essential for businesses to optimize marketing strategies and improve customer retention. This study employs machine learning based clustering techniques to segment customers based on transactional data. By leveraging Recency, Frequency, and Monetary (RFM) analysis, the study compares multiple clustering algorithms to identify distinct customer groups. Experimental results demonstrate that the proposed approach effectively categorizes customers, enabling data-driven decision-making for targeted marketing. These findings highlight the potential of unsupervised learning methods in enhancing business intelligence and customer relationship management.
Causal Graph-Based Root Cause Analysis for Distributed Data Integrity Incidents: A Framework for Cross-System Failure Attribution and Resolution
Distributed data platforms increasingly rely on microservices, event streams, replicated storage, automated deployment pipelines, and heterogeneous integration layers. These environments improve scalability and organizational agility, but they also create a difficult class of incidents: cross-system data integrity failures whose visible symptoms occur far away from their originating cause. A duplicate financial transaction, stale customer profile, corrupted analytical feature, or conflicting replicated record may appear in one system while its causal antecedents lie in delayed message propagation, schema drift, compensating transaction failure, weak consistency, deployment misconfiguration, or an upstream machine-learning service. Conventional monitoring and root cause analysis techniques are often optimized for latency, availability, or infrastructure symptoms, and therefore they struggle to distinguish causal faults from correlated anomalies. This paper proposes CG-RCA-DI, a causal graph-based root cause analysis framework for distributed data integrity incidents. The framework combines system topology, event lineage, observability telemetry, data quality constraints, deployment metadata, and domain policies into a multilayer causal graph. It then applies causal discovery, temporal precedence, counterfactual screening, and graph-based attribution to identify likely root causes and recommend resolution actions. The contribution is a complete reference architecture, an incident model, a causal scoring method, and an evaluation protocol for precision, attribution latency, resolution quality, and auditability. The paper argues that data integrity RCA must move beyond service-level symptom localization toward evidence-grounded causal attribution across application, data, process, and governance layers.
Intelligent Software Architecture Recovery Using Large Language Models and Graph Neural Networks for Legacy System Modernization
Legacy software systems remain central to banking, healthcare, insurance, government, retail, telecommunications, and enterprise resource planning environments, yet many of these systems evolve for years without corresponding architectural documentation. The absence of reliable architectural knowledge increases maintenance cost, slows digital transformation, weakens software reliability, and creates substantial risk during cloud migration, microservices decomposition, regulatory remediation, and DevOps modernization. Traditional software architecture recovery techniques rely heavily on static dependencies, clustering heuristics, call graphs, package structures, and manual expert interpretation. Although these methods provide useful structural views, they often fail to capture the semantic intent, business capability alignment, hidden cross-cutting concerns, and modernization feasibility of legacy modules. This paper proposes an intelligent architecture recovery framework that combines Large Language Models (LLMs) and Graph Neural Networks (GNNs) to recover, explain, and operationalize architectural knowledge from legacy software repositories. The proposed framework constructs a heterogeneous software knowledge graph from source code, build files, configuration artifacts, database scripts, dependency metadata, API traces, commit history, and documentation fragments. LLMs are used to infer semantic roles, business capabilities, domain concepts, anti-patterns, and modernization constraints from code and natural language artifacts, while GNNs learn structural and semantic representations of software entities to identify architectural components, boundary violations, dependency risks, and migration candidates. The paper presents a research-oriented framework, algorithmic workflow, graph schema, training strategy, evaluation protocol, and modernization decision model. Unlike purely review-based studies, the contribution of this paper is a proposed recover-and-modernize architecture intelligence pipeline that transforms legacy software comprehension into actionable modernization recommendations. The framework is intended to support architects and engineering teams in microservices decomposition, cloud migration planning, technical debt prioritization, service boundary discovery, impact analysis, and reliability improvement. The study argues that hybrid LLM-GNN architecture recovery can bridge the gap between structural reverse engineering and semantically grounded modernization governance.
Retrieval-Augmented Software Engineering for Enterprise Codebases: Context-Aware Requirement Traceability, Bug Localization, and Developer Decision Support
Enterprise software engineering increasingly operates under conditions of scale, regulatory exposure, architectural fragmentation, and accelerated delivery pressure. Large codebases are distributed across services, repositories, branches, build pipelines, requirements systems, incident records, design documents, and operational telemetry. In this setting, developers and engineering leaders must answer high-stakes questions: which code implements a requirement, where a defect is likely to be fixed, which architectural dependency explains a failure, and what decision is justified by available engineering evidence. This paper proposes RASE, a retrieval-augmented software engineering framework for context-aware requirement traceability, bug localization, and developer decision support in enterprise codebases. RASE treats software engineering artifacts as a governed, typed, versioned, and auditable knowledge substrate rather than a passive document collection. The framework combines hybrid lexical–dense retrieval, code-aware embeddings, dependency graphs, evidence ranking, retrieval-conditioned generation, and decision governance. Unlike generic retrieval-augmented generation pipelines, RASE introduces artifact-type aware chunking, traceability-preserving metadata, multi-hop retrieval over code and non-code artifacts, and evidence contracts that constrain generated recommendations to retrieved, inspectable sources. The paper presents a research design, architecture, operational workflow, and reproducible evaluation protocol for three core tasks: recovering requirement-to-code links, localizing buggy files from reports and incidents, and supporting developer decisions during design, change impact analysis, and release governance. The proposed evaluation emphasizes recall-oriented retrieval quality, developer-facing usefulness, provenance precision, latency, security, and organizational adoption. The contribution is a research-grade framework that integrates established information retrieval principles, modern code representation learning, secure software development governance, and AI-enabled lifecycle management into a unified enterprise software engineering method. RASE is positioned as an empirical artifact for future controlled industrial validation, with clearly defined research questions, hypotheses, metrics, ablation strategy, and threats to validity.
LLM-Driven DevSecOps for Secure Software Engineering: Integrating Static Analysis, Retrieval-Augmented Generation, and Automated Vulnerability Remediation
The rapid adoption of large language models in software engineering has transformed how developers design, generate, review, test, and maintain code. However, the same automation that accelerates development also introduces security risks when generated patches, dependency upgrades, or code explanations are accepted without systematic verification. This paper proposes an LLM-driven DevSecOps framework that integrates static application security testing, retrieval-augmented generation, secure knowledge grounding, automated vulnerability remediation, human approval workflows, and continuous post-remediation validation. The central thesis is that LLMs should not be treated as autonomous security authorities, but as governed remediation agents operating within evidence-based, policy-constrained, and auditable software delivery pipelines. The framework aligns secure software development with NIST SSDF practices by embedding security activities across planning, implementation, verification, release, and monitoring stages [1]. It also extends prior work on AI-driven software lifecycle governance by positioning defect prediction, automated testing, vulnerability detection, and remediation as mutually reinforcing components rather than isolated quality engineering tasks [2]. The proposed architecture uses static analysis findings as structured triggers, retrieval-augmented generation as an evidence-grounded reasoning layer, and controlled patch generation as a remediation mechanism. The paper further presents a methodological design for evaluating the framework using vulnerability detection precision, remediation correctness, regression safety, developer review burden, mean time to remediate, and auditability. The contribution is a reference architecture for secure AI-assisted engineering that reduces manual triage effort while preserving accountability, traceability, and security assurance.
Scalable Data Pipeline Architecture for Real-Time Supply Chain Analytics Using PySpark and Snowflake
As the volume of data continues to grow in enterprises and the network is becoming increasingly complex, real-time supply chain analytics has emerged as a necessary tool for gaining greater visibility, responsiveness, and insight into the enterprise's operations. Historical data processing architectures have found themselves inadequately capable of managing the volume, velocity and variety of data coming into an enterprise via its supply chain from sources such as enterprise resource planning systems, warehouse management systems, transportation systems, iot devices and digital commerce systems. In this paper, a scalable data pipeline architecture for real-time supply chain analytics using Apache PySpark along with data pipeline component for distributed stream and batch data processing and Snowflake as a cloud-native analytical data warehouse is introduced. Data ingestion is designed to have scalable inbound data delivery rates; the data is ingested by a parallelized transformation flow; scalable storage; and low latency data analytical querying. That means the proposed architecture would enable near real-time operational intelligence. Technical and operational aspects of key architectural elements, such as Data Ingestion frameworks, Processing pipelines, orchestration mechanisms, and Analytical storage layers are explored. The performance evaluation shows that the architecture can process large scale data from supply chain efficiently while being scalable, reliable and cost-effective. The study also demonstrates some of the key considerations for actually implementing these, data governance frameworks, and optimization methods that allow companies to gain actionable insights from the perpetually changing supply chains. The proposed resolution has a modern, cloud-based solution for real-time supply chain analytics and has formed a base for further intelligent and AI-based source chain decision support systems.
Reinforcement Learning-Based Labor Planning Optimization for Dynamic E-Commerce Fulfillment Operations
Labor planning in high-volume e-commerce fulfillment operations requires adaptive allocation strategies that respond to real-time demand fluctuations, staffing constraints, and operational variability across warehouse zones. Traditional workforce scheduling systems rely heavily on rule-based heuristics, historical averaging, and static optimization models that fail to capture the stochastic and dynamic nature of modern fulfillment centers. This paper presents a reinforcement learning (RL) framework for dynamic labor planning optimization in e-commerce fulfillment environments, modeling the allocation problem as a Markov Decision Process (MDP). The proposed framework encodes warehouse operational dynamics into state representations that include queue depths across fulfillment zones, inbound order volume forecasts, worker productivity distributions, shift constraints, and task prioritization weights. The action space represents labor allocation decisions across zones and time intervals, while the reward function is designed to optimize throughput, minimize order latency, and reduce labor imbalance penalties. A Proximal Policy Optimization (PPO) agent is trained using historical operational datasets derived from simulated e-commerce fulfillment logs. The model learns adaptive policies that outperform traditional rule-based heuristics under varying demand scenarios, including peak load conditions and stochastic surges in order volume. The RL agent demonstrates improved responsiveness to dynamic workload shifts, particularly in environments characterized by high uncertainty and non-linear demand patterns. The system architecture integrates enterprise warehouse management system (WMS) APIs for real-time state ingestion and allocation dispatch, enabling closed-loop decision-making. This integration demonstrates the feasibility of deploying reinforcement learning agents in production-grade fulfillment environments with minimal latency overhead. Experimental evaluations indicate that the PPO-based labor planning model achieves significant improvements in key performance metrics, including order fulfillment time reduction, improved zone-level labor utilization balance, and reduced idle workforce percentage. Sensitivity analysis further confirms the robustness of the RL policy under demand volatility and staffing constraints. The findings suggest that reinforcement learning provides a scalable and adaptive approach to labor planning in modern e-commerce ecosystems, enabling intelligent automation of workforce distribution decisions. This research contributes to the growing body of work on AI-driven supply chain optimization and demonstrates practical applicability in real-world warehouse operations.
Microservices Decomposition Strategies for Legacy .NET Monolith Migration: Patterns and Anti-Patterns in Financial Services
Migrating legacy monolithic .NET applications to microservices architectures in financial services environments presents challenges that extend beyond standard decomposition techniques, encompassing regulatory continuity requirements, transaction integrity preservation, and the operational risk of incremental service extraction from tightly coupled codebases. Financial institutions have historically relied on large-scale monolithic systems to support critical operations including payment processing, customer relationship management, loan servicing, risk assessment, compliance reporting, and transaction reconciliation. While these systems have demonstrated reliability over extended periods, they frequently exhibit architectural rigidity, limited scalability, prolonged release cycles, and increasing maintenance complexity. The emergence of cloud-native computing and digital banking ecosystems has intensified the need for architectural modernization, making microservices adoption a strategic priority for organizations seeking agility, resilience, and accelerated innovation. This paper presents a systematic analysis of microservices decomposition strategies applied to legacy .NET monolith migration in financial services contexts. The study investigates domain-driven decomposition, strangler fig migration patterns, anti-corruption layer implementation, event-driven decoupling mechanisms, and service boundary identification techniques. Particular attention is given to the unique operational constraints of financial institutions, including regulatory compliance, auditability, security controls, high transaction volumes, and service-level agreement requirements. The research evaluates the effectiveness of decomposition approaches through architectural quality attributes such as scalability, maintainability, deployment independence, fault isolation, and business alignment. The proposed framework emphasizes domain-driven design principles for identifying bounded contexts and establishing service ownership boundaries. Furthermore, the study explores the role of anti-corruption layers in preserving interoperability between legacy and modernized environments while minimizing dependency propagation. Event-driven architecture patterns are analyzed as mechanisms for reducing coupling and enabling asynchronous communication among distributed services. The paper also examines common anti-patterns encountered during migration initiatives, including distributed monolith formation, improper service granularity, shared database dependencies, excessive synchronous communication, and inadequate governance practices. Results indicate that organizations adopting structured decomposition methodologies achieve substantial improvements in deployment frequency, fault containment, scalability, and operational flexibility. Conversely, migration programs lacking domain-oriented planning frequently encounter integration complexity, performance degradation, and governance challenges. The findings demonstrate that successful modernization requires a balanced combination of technical architecture transformation, organizational readiness, and incremental migration execution strategies. The proposed framework contributes practical guidance for architects, software engineers, and enterprise modernization teams seeking to transform legacy .NET monoliths into resilient microservices ecosystems within highly regulated financial services environments.